As a data scientist, I sometimes get approached by others on questions related to data science. This could be while at work, or at the meetups I organise and attend, or questions on my blog or linkedIn. Through these interactions, I realised there is significant misunderstanding about data science. Misunderstandings arise around the skills needed to practice data science, as well as what data scientists actually do.
Perception of what is needed and done
Many people are of the perception that deep technical and programming abilities, olympiad level math skills, and a PhD are the minimum requirements, and that having such skills and education qualifications will guarantee success in the field. This is slightly unrealistic and misleading, and does not help to mitigate the issue of scarce data science talent, such as those listed here and here.
Similarly, based on my interactions with people, as well as comments online, many perceive that a data scientist’s main job is machine learning, or researching the latest neural network architectures—essentially, Kaggle as a full time job. However, machine learning is just a slice of what data scientists actually do (personally, I find it constitutes < 20% of my day to day work).
How do these perceptions come about?
One hypothesis is the statical fallacy of availability. For the average population, they would probably know about data scientists based on what they’ve seen/heard on the news and articles, or perhaps a course or two on Coursera.
What’s likely to be the background of these data scientists? If it’s from this article on the recent Turing Award for contributions in AI, you’ll find three very distinguished gentlemen who have amazing publishing records and introduced the world to neural networks, backpropogation, CNNs, and RNNs. Or perhaps you read the recent article about how neural networks and reinforcement learning achieved human expert level performance, and found that the team was largely comprised of PhDs. If it’s from a course, the person is likely to have a PhD, and went through deep mathematical proofs on machine learning techniques. Thus, based on what you can think of, or what is available in memory, many people tend to have a skewed perception on what background a data scientist should have.
The same goes for what data scientists actually do. Most of the sexy headlines on data science involve using machine learning to solve (currently) unsolvable problems, everything from research-based (computer games) to very much applied (self-driving cars). In addition, given that the majority of data science courses are on machine learning, its no wonder that the statistical fallacy of availability would skew people towards thinking that machine learning is the be all end all.